Least square method support vector machine-based generalized prediction method in lysozyme fermentation process

The invention discloses a least square method support vector machine-based generalized prediction method in a lysozyme fermentation process. The prediction method comprises the following steps of establishing a non-linear prediction model, and training a least square method support vector machine by using production data with higher yield screened from tank fermentation; performing real-time linearization on the input and output non-linear prediction model, setting a reference trajectory, rolling-optimizing controller design, and intelligently embedding an LS-SVM (least square-support vector machine)-based generalized prediction control algorithm in the lysozyme fermentation process into an upper computer. According to the method, the least square method support vector machine and the generalized prediction control are combined, so the QP problem of time consumption of solving in the solving process with the model is avoided, the operation is simple, the convergence speed is speed, and the precision is high. A genetic algorithm and the rolling optimizing in the generalized prediction control are combined, so the robustness of a system is enhanced, and the lag and disturbance of the system are effectively overcome.